import torch
import torch.nn as nn
import torch.nn.functional as F


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_channel, out_channel, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,
                               kernel_size=(3, 3), stride=(stride, stride), padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channel)
        self.relu = nn.ReLU()
        self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel,
                               kernel_size=(3, 3), stride=(1, 1), padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channel)
        self.downsample = downsample

    def forward(self, x):
        identity = x
        if self.downsample is not None:
            identity = self.downsample(x)

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        out += identity
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_channel, out_channel, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,
                               kernel_size=(1, 1), stride=(1, 1), bias=False)  # squeeze channels
        self.bn1 = nn.BatchNorm2d(out_channel)
        # -----------------------------------------
        self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel,
                               kernel_size=(3, 3), stride=(stride, stride), bias=False, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channel)
        # -----------------------------------------
        self.conv3 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel * self.expansion,
                               kernel_size=(1, 1), stride=(1, 1), bias=False)  # unsqueeze channels
        self.bn3 = nn.BatchNorm2d(out_channel * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample

    def forward(self, x):
        identity = x
        if self.downsample is not None:
            identity = self.downsample(x)

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        out += identity
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self, block, blocks_num, num_classes=1000, include_top=True):
        super(ResNet, self).__init__()
        self.include_top = include_top
        self.in_channel = 64

        self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=(7, 7), stride=(2, 2),
                               padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(self.in_channel)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, blocks_num[0])
        self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2)
        self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2)
        self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2)
        if self.include_top:
            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))  # output size = (1, 1)
            self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')

    def _make_layer(self, block, channel, block_num, stride=1):
        downsample = None
        if stride != 1 or self.in_channel != channel * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=(1, 1),
                          stride=(stride, stride), bias=False),
                nn.BatchNorm2d(channel * block.expansion))

        layers = []
        layers.append(block(self.in_channel, channel, downsample=downsample, stride=stride))
        self.in_channel = channel * block.expansion

        for _ in range(1, block_num):
            layers.append(block(self.in_channel, channel))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        if self.include_top:
            x = self.avgpool(x)
            x = torch.flatten(x, 1)
            x = self.fc(x)

        return x


def resnet34(num_classes=1000, include_top=True):
    return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)


def resnet101(num_classes=1000, include_top=True):
    return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, include_top=include_top)


# 定义ResBlk类，包含两层卷积层及shortcut计算。
class ResBlk(nn.Module):
    '''
    resnet block
    '''

    def __init__(self, ch_in, ch_out, stride=1):
        '''

        :param ch_in:
        :param ch_out:
        :param stride:
        :return:
        '''
        super(ResBlk, self).__init__()
        self.conv1 = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=stride, padding=1)
        self.bn1 = nn.BatchNorm2d(ch_out)
        self.conv2 = nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=stride, padding=1)
        self.bn2 = nn.BatchNorm2d(ch_out)

        # shortcut计算，当输入输出通道不相等时，通过1*1的卷积核，转化为相等的通道数。
        self.shortcut = nn.Sequential()
        if ch_out != ch_in:
            self.shortcut = nn.Sequential(
                nn.Conv2d(ch_in, ch_out, kernel_size=1, stride=stride),
                nn.BatchNorm2d(ch_out)
            )

    # 前项传播运算
    def forward(self, x):
        '''

        :param x:
        :return:
        '''
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        # shortcut
        # extra module:[b,ch_in,h,w] => [b,ch_out, h,w]
        out += self.shortcut(x)
        out = F.relu(out)
        return out


# ResNet18结构框架
class ResNet18(nn.Module):
    def __init__(self):
        super(ResNet18, self).__init__()

        self.pre = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=0),
            nn.BatchNorm2d(64)
        )

        # followed blocks
        # 重复的layer，分别为2个resblk.
        self.blk1 = self.make_layer(64, 64, 2, stride=1)
        self.blk2 = self.make_layer(64, 128, 2, stride=1)
        self.blk3 = self.make_layer(128, 256, 2, stride=1)
        self.blk4 = self.make_layer(256, 512, 2, stride=1)

        self.outlayer = nn.Linear(512 * 1 * 1, 10)

    def make_layer(self, ch_in, ch_out, block_num, stride=1):
        '''
        #构建layer，包含多个ResBlk
        :param ch_in:
        :param ch_out:
        :param block_num:为每个blk的个数
        :param stride:
        :return:
        '''
        layers = []
        layers.append(ResBlk(ch_in, ch_out, stride))

        for i in range(1, block_num):
            layers.append(ResBlk(ch_out, ch_out))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.pre(x)

        # [b,64,h,w] => [b,1024,h,w]
        x = self.blk1(x)
        x = self.blk2(x)
        x = self.blk3(x)
        x = self.blk4(x)

        # [b,512,h,w] => [b,512,1,1]
        x = F.adaptive_avg_pool2d(x, [1, 1])
        # print('after pool:', x.shape)
        x = x.view(x.size(0), -1)
        x = self.outlayer(x)

        return x


class mnist_Net(nn.Module):
    def __init__(self):
        super(mnist_Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, (5, 5))
        self.pool1 = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, (5, 5))
        self.pool2 = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(16 * 4 * 4, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.pool1(x)
        x = F.relu(self.conv2(x))
        x = self.pool2(x)
        x = x.view(-1, 16 * 4 * 4)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, p=0.3)
        x = F.relu(self.fc2(x))
        x = F.dropout(x, p=0.1)
        x = self.fc3(x)
        return x


class MyMnistNet(nn.Module):
    def __init__(self):
        super(MyMnistNet, self).__init__()
        # 1 input image channel, 6 output channels, 5x5 square convolution
        # kernel
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.conv2 = nn.Conv2d(6, 16, 5)
        # an affine operation: y = Wx + b
        self.fc1 = nn.Linear(16 * 4 * 4, 120)  # 4*4 from image dimension
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        # Max pooling over a (2, 2) window
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        # If the size is a square, you can specify with a single number
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        # x = F.relu(self.conv2(x))

        x = torch.flatten(x, 1)  # flatten all dimensions except the batch dimension
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        x = F.log_softmax(x, dim=1)  # 计算log(softmax(x))
        return x

